Statistical multisite simulations of summertime precipitation over South Korea and its future change based on observational data

Kim, Jiwon; Kim, Kwang‐Yul; Kim, Maeng-Ki; Cho, Chun-Ho; Lee, Youngjo; Lee, Jae Yong

doi:10.1007/s13143-013-0061-7

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…For climate variables, precipitation forecast data are available from the Korea Meteorological Agency (KMA). However, this study applies the research approach used by Kim et al (2013). The problem with the KMA forecasting model ( 2012) is that it significantly underestimates the typical peak of summer precipitation in Korea, thus making future precipitation estimates highly unreliable.…”

Section: Forecasting Future Damage Costsmentioning

confidence: 99%

“…The problem with the KMA forecasting model ( 2012) is that it significantly underestimates the typical peak of summer precipitation in Korea, thus making future precipitation estimates highly unreliable. In contrast, the weather generator developed by Kim et al (2013), which uses a cyclostationary empirical orthogonal function (CSEOF) technique, faithfully reproduces the peak summer daily precipitation. Based on the synthetic time series and annually repeating evolution patterns, 100 sets of synthetic summer daily precipitation records for 57 stations from 2013 to 2060 are generated.…”

Section: Forecasting Future Damage Costsmentioning

confidence: 99%

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Estimating Damage Costs from Natural Disasters in Korea

2017

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This study forecasts the future cost of direct economic damages caused by natural disasters in Korea by using panel data regression. The authors first develop a balanced panel data spanning 2001-2012 for all 16 metropolitan cities and provinces in Korea. The result shows that a 1% increase in annual precipitation and impervious surfaces increases damage costs by 4.52 and 1.74%, respectively. In addition, the financial independence of local governments is negatively correlated with damage costs. The maximum annual damage costs from natural disasters through 2060 are estimated to be US$20.9 billion, which would be 1.03% of future Korean gross domestic product (GDP). Among the regions, Gangwon-do, Jeollabuk-do, and Jeollanam-do, which have a high percentage of impervious surfaces and a low financial independence rate, are expected to be the most vulnerable to natural disasters. Their estimated maximum annual damage costs are above US $5 billion by 2060, which would exceed 7% of their gross regional domestic product (GRDP). This study is the first attempt to estimate and forecast the damage costs from natural disasters using region-specific weather data in Korea. It suggests a need for a well-designed natural disaster management plan at both the central and local government levels.

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Section: Forecasting Future Damage Costsmentioning

confidence: 99%

Section: Forecasting Future Damage Costsmentioning

confidence: 99%

Estimating Damage Costs from Natural Disasters in Korea

2017

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“…Zhang et al 1997;Fyfe et al 1999). EOFs have not been widely applied to weather generators in the past, but cyclostationary EOF analysis was used by Kim et al (2013) to model summer rainfall in Korea. Our model differs from theirs in a number of ways, including: our use of periodically extended EOFs to capture the low-frequency variability of weather variables to overcome the problem of overdispersion; our use of autoregressive models at each individual site, rather than over the whole domain; our integrated approach to the modelling of extremes as opposed to modelling extremes as events always occurring simultaneously over predefined clusters of sites.…”

Section: Introductionmentioning

confidence: 99%

IMAGE: a multivariate multi-site stochastic weather generator for European weather and climate

Sparks

Hardwick

Schmid

et al. 2017

Stoch Environ Res Risk Assess

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Capturing the spatial and temporal correlation of multiple variables in a weather generator is challenging. A new massively multi-site, multivariate daily stochastic weather generator called IMAGE is presented here. It models temperature and precipitation variables as latent Gaussian variables with temporal behaviour governed by an auto-regressive model whose residuals and parameters are correlated through resampling of principle component time series of empirical orthogonal function modes. A case study using European climate data demonstrates the model's ability to reproduce extreme events of temperature and precipitation. The ability to capture the spatial and temporal extent of extremes using a modified Climate Extremes Index is demonstrated. Importantly, the model generates events covering not observed temporal and spatial scales giving new insights for risk management purposes.

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Theoretical foundation of cyclostationary EOF analysis for geophysical and climatic variables: Concepts and examples

Kim

Hamlington

2015

Earth-Science Reviews

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Statistical multisite simulations of summertime precipitation over South Korea and its future change based on observational data

Cited by 5 publications

References 28 publications

Estimating Damage Costs from Natural Disasters in Korea

Estimating Damage Costs from Natural Disasters in Korea

IMAGE: a multivariate multi-site stochastic weather generator for European weather and climate

Theoretical foundation of cyclostationary EOF analysis for geophysical and climatic variables: Concepts and examples

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